{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/model-serving"},"x-facet":{"type":"skill","slug":"model-serving","display":"Model Serving","count":16},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. 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Together, you will work towards one core goal: helping hosts improve occupancy and earnings through a smart, dynamic, and data-driven pricing strategy.</p>\n<p><strong>Our Tech Stack</strong></p>\n<ul>\n<li>Data Storage &amp; Querying: S3, Redshift (with decentralized data sharing), Athena, and DuckDB.</li>\n<li>ML &amp; Model Serving: MLflow, SageMaker, and deployment APIs for model lifecycle management.</li>\n<li>Cloud &amp; DevOps: Terraform, Docker, Jenkins, and AWS EKS (Kubernetes) for scalable, resilient systems.</li>\n<li>Monitoring: ELK, Grafana, Looker, OpsGenie, and in-house tools for full visibility.</li>\n<li>Ingestion: Kafka-based event systems and tools like Airbyte and Fivetran for smooth third-party integrations.</li>\n<li>Automation &amp; AI: Extensive use of AI tools like Claude, Copilot, and Codex.</li>\n</ul>\n<p><strong>Your role in this journey</strong></p>\n<p>As a Data Ops Engineer – Revenue Management, you&#39;ll be the engineering backbone that enables our Data Scientists to move from experimentation to production. You bridge the gap between data science models and reliable, scalable production systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Support model deployment and serving: help deploy pricing and demand models into production, building and maintaining APIs and serving infrastructure.</li>\n<li>Build and operate production pipelines: ensure data flows reliably from source to model to output, with proper monitoring and alerting.</li>\n<li>Collaborate cross-functionally: work closely with Data Scientists, Analysts, and Engineering teams to turn prototypes into production-ready solutions.</li>\n<li>Own infrastructure and tooling: set up and maintain the environments, CI/CD pipelines, and infrastructure that the team depends on.</li>\n<li>Ensure operational excellence by implementing monitoring, automated testing, and observability across the team&#39;s production systems.</li>\n<li>Migrate and productionize POC: turn experimental code into robust, maintainable Python applications.</li>\n<li>Ensure data quality, consistency, and documentation across revenue management metrics and datasets.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Impact: Shape the future of travel with products used by millions of guests and thousands of hosts.</li>\n<li>Learning: Grow professionally in a culture that thrives on curiosity and feedback.</li>\n<li>Great People: Join a team of smart, motivated, and international colleagues who challenge and support each other.</li>\n<li>Technology: Work in a modern tech environment.</li>\n<li>Flexibility: Work a hybrid setup with 50% in-office time for collaboration, and spend up to 8 weeks a year from other inspiring locations.</li>\n<li>Perks on Top: Of course, we also offer travel benefits, gym discounts, and other perks to keep you energized.</li>\n</ul>\n<p><strong>Experience</strong></p>\n<ul>\n<li>4+ years of experience in Software Engineering, Data Engineering, DevOps, or MLOps.</li>\n<li>Strong hands-on skills in Python , you write clean, production-quality code.</li>\n<li>Experience with CI/CD, Docker, and infrastructure-as-code (e.g., Terraform).</li>\n<li>Familiarity with cloud platforms (AWS preferred) and deploying services in production.</li>\n<li>Exposure to or interest in ML model deployment (MLflow, SageMaker, or similar) is a strong plus.</li>\n<li>Desire to learn and use cutting-edge LLM tools and agents to improve your and the entire team&#39;s productivity.</li>\n<li>A proactive, hands-on mindset: you take ownership, spot problems, and drive solutions forward.</li>\n</ul>\n<p><strong>How to apply</strong></p>\n<p>If you&#39;re excited about this opportunity, please submit your application on our careers page!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8b447835-74a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Holidu Hosts GmbH","sameAs":"https://holidu.jobs.personio.com","logo":"https://logos.yubhub.co/holidu.jobs.personio.com.png"},"x-apply-url":"https://holidu.jobs.personio.com/job/2597559","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"Full-time","x-salary-range":null,"x-skills-required":["Python","CI/CD","Docker","Terraform","Cloud platforms (AWS preferred)","ML model deployment (MLflow, SageMaker, or similar)"],"x-skills-preferred":["AI tools like Claude, Copilot, and Codex","Data Storage & Querying (S3, Redshift, Athena, DuckDB)","ML & Model Serving (MLflow, SageMaker, deployment APIs)","Cloud & DevOps (Terraform, Docker, Jenkins, AWS EKS)","Monitoring (ELK, Grafana, Looker, OpsGenie, in-house tools)","Ingestion (Kafka-based event systems, Airbyte, Fivetran)"],"datePosted":"2026-04-18T22:09:42.352Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Munich, Germany"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, CI/CD, Docker, Terraform, Cloud platforms (AWS preferred), ML model deployment (MLflow, SageMaker, or similar), AI tools like Claude, Copilot, and Codex, Data Storage & Querying (S3, Redshift, Athena, DuckDB), ML & Model Serving (MLflow, SageMaker, deployment APIs), Cloud & DevOps (Terraform, Docker, Jenkins, AWS EKS), Monitoring (ELK, Grafana, Looker, OpsGenie, in-house tools), Ingestion (Kafka-based event systems, Airbyte, Fivetran)"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_648f4814-708"},"title":"Senior Software Engineer, Machine Learning (Commerce)","description":"<p>We are looking for a Senior Machine Learning Engineer to join our Revenue ML team at Discord. This role sits at the intersection of Discord&#39;s two most strategic revenue pillars , our growing 1P Shop and our newly launched Game Commerce platform. 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We value impact and believe that the highest-impact AI research will be big science. We work as a single cohesive team on just a few large-scale research efforts and value communication skills.</p>\n<p>If you&#39;re interested in this role, please submit an application even if you don&#39;t believe you meet every single qualification. We encourage diversity and strive to include a range of diverse perspectives on our team.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_709b405a-48b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5113224008","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$325,000-$485,000 USD","x-skills-required":["distributed systems","infrastructure","reliability","Service Level Objectives","monitoring and observability systems","high-availability serving infrastructure","incident response","safeguard model serving"],"x-skills-preferred":["large-scale model serving or training infrastructure","ML hardware accelerators","ML-specific networking optimizations","AI-specific observability tools and frameworks","chaos engineering and systematic resilience testing","open-source infrastructure or ML tooling"],"datePosted":"2026-04-18T15:52:16.313Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY | Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"distributed systems, infrastructure, reliability, Service Level Objectives, monitoring and observability systems, high-availability serving infrastructure, incident response, safeguard model serving, large-scale model serving or training infrastructure, ML hardware accelerators, ML-specific networking optimizations, AI-specific observability tools and frameworks, chaos engineering and systematic resilience testing, open-source infrastructure or ML tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":325000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_e3b1c38b-ef1"},"title":"Staff Software Engineer, Communication Products","description":"<p>Job Title: Staff Software Engineer, Communication Products</p>\n<p>We are seeking a highly skilled and experienced Staff Software Engineer to join our Communication Products team. As a Staff Engineer, you will be responsible for leading the technical vision for ML-powered messaging features, architecting and delivering intelligent capabilities end-to-end, and partnering deeply with ML and product teams.</p>\n<p>The Difference You Will Make:</p>\n<p>As a Staff Engineer on the team, you will define and drive the technical strategy for integrating ML capabilities into Airbnb&#39;s messaging products, including smart replies, message classification, content moderation, translation, and conversational assistance. You will also own the full lifecycle of ML-powered features: from prototyping and experimentation through launch, monitoring, and iteration.</p>\n<p>A Typical Day:</p>\n<ul>\n<li>Design, build, and operate the systems that serve ML models within the messaging stack, with a focus on latency, reliability, and scalability</li>\n<li>Write and review technical designs that solve large, open-ended problems at the intersection of ML and product engineering without clearly-known solutions</li>\n<li>Partner with ML, data science, and product teams to identify high-value opportunities, establish evaluation criteria, and close the gap between offline model performance and production impact</li>\n<li>Collaborate with other engineers and cross-functional partners across Messaging, Trust &amp; Safety, Localization, and Platform organizations to align on long-term technical solutions</li>\n<li>Mentor, guide, advocate, and support the career growth of individual contributors</li>\n<li>Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation</li>\n</ul>\n<p>Your Expertise:</p>\n<ul>\n<li>9+ years of relevant engineering hands-on work experience</li>\n<li>Bachelors, Masters, or PhD in CS or related field</li>\n<li>Demonstrated experience building and shipping ML-powered product features in production environments, including model serving, feature pipelines, online/offline evaluation, and monitoring</li>\n<li>Exceptional architecture abilities and experience with architectural patterns of large, high-scale applications</li>\n<li>Familiarity with NLP/NLU techniques and large language models, particularly as applied to messaging, conversational AI, or content understanding</li>\n<li>Shipped several large-scale projects with multiple dependencies across teams, specifically at the intersection of ML infrastructure and product engineering</li>\n<li>Technical leadership and strong communication skills with the ability to translate between ML research, product goals, and engineering execution</li>\n<li>Experience operating distributed, real-time systems at scale with high reliability requirements</li>\n<li>Experience with real-time messaging systems or event-driven architectures</li>\n<li>Familiarity with ML infrastructure at scale (e.g., feature stores, model registries, online inference platforms)</li>\n<li>Prior work on trust &amp; safety, content moderation, or internationalization in a messaging context</li>\n<li>Experience with LLM-based product features, including prompt engineering, retrieval-augmented generation, or fine-tuning</li>\n</ul>\n<p>How We&#39;ll Take Care of You:</p>\n<p>Our job titles may span more than one career level. 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You will work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Designing and building the core platform infrastructure that supports our customer-facing product features</li>\n<li>Ensuring the reliability, security, and scalability of the backend distributed systems that power all aspects of our product</li>\n<li>Translating product requirements into user interfaces and backend distributed system design and owning end-to-end implementation</li>\n</ul>\n<p>We look for:</p>\n<ul>\n<li>4+ years of hands-on programming experience with at least one modern language such as Python, Scala, Go, or C++</li>\n<li>Strong sense of distributed systems design and experience building large-scale systems</li>\n<li>Experience building ML platform systems for applications in the ML model development lifecycle such as data preparation, model training, model evaluation, and model serving</li>\n<li>Direct experience developing ML models is a plus but not required</li>\n<li>Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability</li>\n<li>Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders</li>\n</ul>\n<p>Pay Range Transparency</p>\n<p>Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. 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You will design, build, and maintain scalable data web applications, AI chatbots, and custom operational interfaces using frameworks like Streamlit, React, and FastAPI. By leveraging Databricks Apps&#39; serverless infrastructure, you will eliminate the need for external hosting and empower business users to make informed decisions by bridging the gap between raw data and solutions using your engineering prowess, Databricks apps, Databricks SQL, Lakebase and AgentBricks.</p>\n<p>The Impact You Will Have:</p>\n<ul>\n<li>Build: You will design and develop robust frontend interfaces and API backends (e.g., FastAPI routing user queries to model-serving endpoints). 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Experience with frameworks like Streamlit, Dash, Flask, FastAPI, React, or Express is required.</li>\n</ul>\n<ul>\n<li>You know the Databricks ecosystem. Familiarity with Unity Catalog, Databricks SQL, Databricks SDK for Python, and Model Serving is highly preferred.</li>\n</ul>\n<ul>\n<li>You have built for scale and security. Experience with CI/CD tools, Infrastructure as Code (specifically Databricks Asset Bundles), and implementing secure OAuth flows.</li>\n</ul>\n<ul>\n<li>You are passionate about applying AI. Experience integrating LLMs or Mosaic AI Agent Frameworks into application backends to deliver intelligent chat and RAG solutions.</li>\n</ul>\n<ul>\n<li>You excel in a collaborative environment. You can translate stakeholder requirements into intuitive user interfaces, working through dependencies and troubleshooting deployment errors or telemetry logs.</li>\n</ul>\n<p>Pay Range Transparency Databricks is committed to fair and equitable compensation practices. 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The successful candidate will work closely with AI Engineers, Data Scientists, Platform Engineers, Security, and Product partners to deliver resilient, cloud native AI platforms in a highly regulated environment.</p>\n<p><strong>Key Responsibilities</strong></p>\n<ul>\n<li>Design, build, and operate AI-focused infrastructure platforms supporting model development, training, evaluation, and inference.</li>\n<li>Engineer scalable, reliable, and secure cloud-native services to support AI workloads across AWS, Azure, and hybrid environments.</li>\n<li>Partner with AI Engineering and Data Science teams to improve developer experience, performance, and operational stability of AI systems.</li>\n<li>Enable production deployment of ML models and LLMs within governed enterprise environments, aligned with firmwide risk and compliance standards.</li>\n<li>Implement and maintain infrastructure as code and automation to ensure repeatable, auditable platform provisioning.</li>\n<li>Build and operate observability, monitoring, and alerting solutions for AI platforms, ensuring availability, performance, and cost transparency.</li>\n<li>Collaborate with Security and Risk partners to integrate identity, access controls, data protection, and governance into AI infrastructure.</li>\n<li>Contribute to architectural decisions and technical standards for AI platforms across Aladdin.</li>\n<li>Participate in on-call rotations and operational support as required for critical platforms.</li>\n<li>Continuously evaluate emerging AI infrastructure technologies and apply them pragmatically within BlackRock’s enterprise context.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Strong experience in cloud infrastructure, platform engineering, or systems engineering roles.</li>\n<li>4+ hands-on expertise with AWS and/or Azure and/or GCP, including Azure ML, Azure Foundry, AWS Bedrock, Google Vertex, as well as cloud compute, networking, storage, and security services.</li>\n<li>Understanding of ML platform operations and governance concepts, including model deployment strategies, lifecycle management, monitoring/observability, and Disaster Recovery</li>\n<li>Experience supporting LLMs, generative AI platforms, or model serving infrastructure.</li>\n<li>Experience supporting AI and machine learning workloads, with exposure to managed compute for model training and fine-tuning, experimentation over large datasets, and end-to-end MLOps pipeline flow including data ingestion, training, validation, and deployment.</li>\n<li>Proficiency with Infrastructure as Code tools (e.g., Terraform, ARM/Bicep, CloudFormation).</li>\n<li>Strong programming or scripting skills (e.g., Python, Bash, or similar).</li>\n<li>Experience building and operating containerized and Kubernetes-based platforms.</li>\n<li>Solid understanding of reliability, scalability, observability, and operational best practices.</li>\n<li>Ability to work effectively in cross-functional teams and communicate complex technical concepts clearly.</li>\n</ul>\n<p><strong>Our Benefits</strong></p>\n<p>To help you stay energized, engaged, and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge, and be there for the people you care about.</p>\n<p><strong>Our Hybrid Work Model</strong></p>\n<p>BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_69369815-a11","directApply":true,"hiringOrganization":{"@type":"Organization","name":"BlackRock","sameAs":"https://jobs.workable.com","logo":"https://logos.yubhub.co/view.com.png"},"x-apply-url":"https://jobs.workable.com/view/2JsY2bUdeEEzUfhn796RPb/associate%2Fvice-president%2C-ai-infrastructure-engineer-in-edinburgh-at-blackrock","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["AWS","Azure","GCP","Cloud compute","Networking","Storage","Security services","ML platform operations","Governance concepts","Model deployment strategies","Lifecycle management","Monitoring/observability","Disaster Recovery","LLMs","Generative AI platforms","Model serving infrastructure","AI and machine learning workloads","Managed compute","Fine-tuning","Experimentation","End-to-end MLOps pipeline flow","Data ingestion","Training","Validation","Deployment","Infrastructure as Code","Terraform","ARM/Bicep","CloudFormation","Programming","Scripting","Containerized and Kubernetes-based platforms","Reliability","Scalability","Observability","Operational best practices"],"x-skills-preferred":["GPU or accelerator-based infrastructure","Financial services or highly regulated industries","Multicloud architectures and enterprise governance requirements"],"datePosted":"2026-03-09T16:39:47.983Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Edinburgh, Scotland"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Finance","skills":"AWS, Azure, GCP, Cloud compute, Networking, Storage, Security services, ML platform operations, Governance concepts, Model deployment strategies, Lifecycle management, Monitoring/observability, Disaster Recovery, LLMs, Generative AI platforms, Model serving infrastructure, AI and machine learning workloads, Managed compute, Fine-tuning, Experimentation, End-to-end MLOps pipeline flow, Data ingestion, Training, Validation, Deployment, Infrastructure as Code, Terraform, ARM/Bicep, CloudFormation, Programming, Scripting, Containerized and Kubernetes-based platforms, Reliability, Scalability, Observability, Operational best practices, GPU or accelerator-based infrastructure, Financial services or highly regulated industries, Multicloud architectures and enterprise governance requirements"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_73ff6f07-c0e"},"title":"Staff Software Engineer, AI Reliability Engineering","description":"<p><strong>About Anthropic</strong></p>\n<p>Anthropic&#39;s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.</p>\n<p><strong>About the Role</strong></p>\n<p>Claude has your back. AIRE has Claude&#39;s. Help us keep Claude reliable for everyone who depends on it.</p>\n<p>AIRE (AI Reliability Engineering) partners with teams across Anthropic to improve reliability across our most critical serving paths -- every hop from the SDK through our network, API layers, serving infrastructure, and accelerators and back. We jump into the trenches alongside partner teams to make the systems that deliver Claude more robust and resilient, be it during an incident or collaborating on projects.</p>\n<p>Reliability here is an emergent phenomenon that transcends any single team&#39;s boundaries, so someone has to zoom out and look at the whole picture. That&#39;s us -- and it means few teams at Anthropic offer this kind of dynamic, cross-cutting exposure to the systems that matter most.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Develop appropriate Service Level Objectives for large language model serving systems, balancing availability and latency with development velocity.</li>\n<li>Design and implement monitoring and observability systems across the token path.</li>\n<li>Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers</li>\n<li>Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.</li>\n<li>Support the reliability of safeguard model serving -- critical for both site reliability and Anthropic&#39;s safety commitments.</li>\n</ul>\n<p><strong>You may be a good fit if you</strong></p>\n<ul>\n<li>Have strong distributed systems, infrastructure, or reliability backgrounds -- we&#39;re looking for reliability-minded software engineers and SREs.</li>\n<li>Are curious and brave -- comfortable jumping into unfamiliar systems during an incident and helping drive resolution even when you don&#39;t have deep expertise yet.</li>\n<li>Think holistically about how systems compose and where the seams are.</li>\n<li>Can build lasting relationships across teams -- our engagement model depends on being welcomed as teammates, not outsiders with opinions.</li>\n<li>Care about users and feel ownership over outcomes, even for systems you don&#39;t own.</li>\n<li>Have excellent communication and collaboration skills -- you&#39;ll be partnering across the entire company.</li>\n<li>Bring diverse experience -- the team&#39;s strength comes from people who&#39;ve built product stacks, scaled databases, run massive distributed systems, and everything in between.</li>\n</ul>\n<p><strong>Strong candidates may also</strong></p>\n<ul>\n<li>Have been an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems</li>\n<li>Have experience operating large-scale model serving or training infrastructure (&gt;1000 GPUs).</li>\n<li>Have experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).</li>\n<li>Understand ML-specific networking optimizations like RDMA and InfiniBand.</li>\n<li>Have expertise in AI-specific observability tools and frameworks.</li>\n<li>Have experience with chaos engineering and systematic resilience testing.</li>\n<li>Have contributed to open-source infrastructure or ML tooling.</li>\n</ul>\n<p><strong>Logistics</strong></p>\n<p><strong>Education requirements:</strong> We require at least a Bachelor&#39;s degree in a related field or equivalent experience. <strong>Location-based hybrid policy:</strong> Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship</strong></p>\n<p>We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification.</strong></p>\n<p>Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</p>\n<p><strong>Your safety matters to us.</strong></p>\n<p>To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links—visit anthropic.com/careers directly for confirmed position openings.</p>\n<p><strong>How we&#39;re different</strong></p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. 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However, some roles may require more time in our offices.</p>\n<p><strong>Visa sponsorship:</strong> We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.</p>\n<p><strong>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work.</strong></p>\n<p><strong>Your safety matters to us. 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